Prediction of the protein structural class by specific peptide frequencies.
We evaluated the i-peptides occurrence frequency in the protein sequences belonging to the two datasets which include proteins with a sequence similarity lower than 25% and 40%, respectively. We worked out a new structural class prediction algorithm using the most frequent i-peptides (with i=2, 3, 4), which characterize the four structural classes. Using the tri-peptides, much more able to gain structural information from sequences compared to the di-peptides, the best results were obtained. Compared to the other methods, similarly founded on peptide occurrence frequencies, our method achieves the best prediction accuracy. We compared it also with methods founded on more sophisticated computational approaches.
Costantini
Susan
S
Istituto di Scienze dell'Alimentazione, Avellino, Italy.
Facchiano
Angelo M
AM
D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry*; D011237:Predictive Value of Tests; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D020539:Sequence Analysis, Protein / Q000379:methods; D017386:Sequence Homology, Amino Acid
D010455:Peptides; D011506:Proteins